Overview

Dataset statistics

Number of variables28
Number of observations15848
Missing cells15848
Missing cells (%)3.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 MiB
Average record size in memory232.0 B

Variable types

Categorical11
DateTime1
TimeSeries13
Numeric3

Timeseries statistics

Number of series13
Time series length15848
Starting point2000-01-01 00:00:00
Ending point2010-12-31 00:00:00
Period6 hours, 5 minutes and 1.46 second
2026-01-13T18:34:38.483221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:39.312551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Alerts

State Code has constant value "4"Constant
County Code has constant value "19"Constant
Site Num has constant value "1011"Constant
Address has constant value "1237 S. BEVERLY , TUCSON"Constant
State has constant value "Arizona"Constant
County has constant value "Pima"Constant
City has constant value "Tucson"Constant
NO2 Units has constant value "Parts per billion"Constant
O3 Units has constant value "Parts per million"Constant
SO2 Units has constant value "Parts per billion"Constant
CO Units has constant value "Parts per million"Constant
CO 1st Max Value is highly overall correlated with CO AQI and 5 other fieldsHigh correlation
CO AQI is highly overall correlated with CO 1st Max Value and 5 other fieldsHigh correlation
CO Mean is highly overall correlated with CO 1st Max Value and 5 other fieldsHigh correlation
NO2 1st Max Value is highly overall correlated with CO 1st Max Value and 4 other fieldsHigh correlation
NO2 AQI is highly overall correlated with CO 1st Max Value and 4 other fieldsHigh correlation
NO2 Mean is highly overall correlated with CO 1st Max Value and 5 other fieldsHigh correlation
O3 1st Max Value is highly overall correlated with O3 AQI and 1 other fieldsHigh correlation
O3 AQI is highly overall correlated with O3 1st Max Value and 1 other fieldsHigh correlation
O3 Mean is highly overall correlated with CO 1st Max Value and 5 other fieldsHigh correlation
SO2 1st Max Hour is highly overall correlated with SO2 AQIHigh correlation
SO2 1st Max Value is highly overall correlated with SO2 AQI and 1 other fieldsHigh correlation
SO2 AQI is highly overall correlated with SO2 1st Max Hour and 2 other fieldsHigh correlation
SO2 Mean is highly overall correlated with SO2 1st Max Value and 1 other fieldsHigh correlation
SO2 AQI has 7924 (50.0%) missing valuesMissing
CO AQI has 7924 (50.0%) missing valuesMissing
NO2 Mean is non stationaryNon stationary
NO2 1st Max Value is non stationaryNon stationary
NO2 1st Max Hour is non stationaryNon stationary
NO2 AQI is non stationaryNon stationary
O3 Mean is non stationaryNon stationary
O3 1st Max Value is non stationaryNon stationary
O3 1st Max Hour is non stationaryNon stationary
O3 AQI is non stationaryNon stationary
SO2 Mean is non stationaryNon stationary
SO2 1st Max Value is non stationaryNon stationary
SO2 1st Max Hour is non stationaryNon stationary
CO Mean is non stationaryNon stationary
CO AQI is non stationaryNon stationary
NO2 Mean is seasonalSeasonal
NO2 1st Max Value is seasonalSeasonal
NO2 1st Max Hour is seasonalSeasonal
NO2 AQI is seasonalSeasonal
O3 Mean is seasonalSeasonal
O3 1st Max Value is seasonalSeasonal
O3 1st Max Hour is seasonalSeasonal
O3 AQI is seasonalSeasonal
SO2 Mean is seasonalSeasonal
SO2 1st Max Value is seasonalSeasonal
SO2 1st Max Hour is seasonalSeasonal
CO Mean is seasonalSeasonal
CO AQI is seasonalSeasonal
NO2 1st Max Hour has 1628 (10.3%) zerosZeros
SO2 Mean has 678 (4.3%) zerosZeros
SO2 1st Max Value has 678 (4.3%) zerosZeros
SO2 1st Max Hour has 1930 (12.2%) zerosZeros
SO2 AQI has 456 (2.9%) zerosZeros
CO 1st Max Hour has 4740 (29.9%) zerosZeros

Reproduction

Analysis started2026-01-13 18:33:46.745758
Analysis finished2026-01-13 18:34:37.848193
Duration51.1 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

State Code
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size247.6 KiB
4
15848 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15848
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
415848
100.0%

Length

2026-01-13T18:34:39.651903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-13T18:34:39.699131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
415848
100.0%

Most occurring characters

ValueCountFrequency (%)
415848
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)15848
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
415848
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)15848
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
415848
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)15848
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
415848
100.0%

County Code
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size247.6 KiB
19
15848 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters31696
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row19
2nd row19
3rd row19
4th row19
5th row19

Common Values

ValueCountFrequency (%)
1915848
100.0%

Length

2026-01-13T18:34:39.755813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-13T18:34:39.801381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1915848
100.0%

Most occurring characters

ValueCountFrequency (%)
115848
50.0%
915848
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)31696
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
115848
50.0%
915848
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)31696
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
115848
50.0%
915848
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)31696
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
115848
50.0%
915848
50.0%

Site Num
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size247.6 KiB
1011
15848 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters63392
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1011
2nd row1011
3rd row1011
4th row1011
5th row1011

Common Values

ValueCountFrequency (%)
101115848
100.0%

Length

2026-01-13T18:34:39.858269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-13T18:34:39.903820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
101115848
100.0%

Most occurring characters

ValueCountFrequency (%)
147544
75.0%
015848
 
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)63392
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
147544
75.0%
015848
 
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)63392
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
147544
75.0%
015848
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)63392
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
147544
75.0%
015848
 
25.0%

Address
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size247.6 KiB
1237 S. BEVERLY , TUCSON
15848 

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters380352
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1237 S. BEVERLY , TUCSON
2nd row1237 S. BEVERLY , TUCSON
3rd row1237 S. BEVERLY , TUCSON
4th row1237 S. BEVERLY , TUCSON
5th row1237 S. BEVERLY , TUCSON

Common Values

ValueCountFrequency (%)
1237 S. BEVERLY , TUCSON15848
100.0%

Length

2026-01-13T18:34:39.951678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-13T18:34:39.994131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
123715848
20.0%
s15848
20.0%
beverly15848
20.0%
15848
20.0%
tucson15848
20.0%

Most occurring characters

ValueCountFrequency (%)
63392
16.7%
S31696
 
8.3%
E31696
 
8.3%
315848
 
4.2%
215848
 
4.2%
115848
 
4.2%
715848
 
4.2%
.15848
 
4.2%
B15848
 
4.2%
V15848
 
4.2%
Other values (9)142632
37.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)380352
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
63392
16.7%
S31696
 
8.3%
E31696
 
8.3%
315848
 
4.2%
215848
 
4.2%
115848
 
4.2%
715848
 
4.2%
.15848
 
4.2%
B15848
 
4.2%
V15848
 
4.2%
Other values (9)142632
37.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)380352
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
63392
16.7%
S31696
 
8.3%
E31696
 
8.3%
315848
 
4.2%
215848
 
4.2%
115848
 
4.2%
715848
 
4.2%
.15848
 
4.2%
B15848
 
4.2%
V15848
 
4.2%
Other values (9)142632
37.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)380352
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
63392
16.7%
S31696
 
8.3%
E31696
 
8.3%
315848
 
4.2%
215848
 
4.2%
115848
 
4.2%
715848
 
4.2%
.15848
 
4.2%
B15848
 
4.2%
V15848
 
4.2%
Other values (9)142632
37.5%

State
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size247.6 KiB
Arizona
15848 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters110936
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowArizona
2nd rowArizona
3rd rowArizona
4th rowArizona
5th rowArizona

Common Values

ValueCountFrequency (%)
Arizona15848
100.0%

Length

2026-01-13T18:34:40.047015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-13T18:34:40.090054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
arizona15848
100.0%

Most occurring characters

ValueCountFrequency (%)
A15848
14.3%
r15848
14.3%
i15848
14.3%
z15848
14.3%
o15848
14.3%
n15848
14.3%
a15848
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)110936
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A15848
14.3%
r15848
14.3%
i15848
14.3%
z15848
14.3%
o15848
14.3%
n15848
14.3%
a15848
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)110936
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A15848
14.3%
r15848
14.3%
i15848
14.3%
z15848
14.3%
o15848
14.3%
n15848
14.3%
a15848
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)110936
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A15848
14.3%
r15848
14.3%
i15848
14.3%
z15848
14.3%
o15848
14.3%
n15848
14.3%
a15848
14.3%

County
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size247.6 KiB
Pima
15848 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters63392
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPima
2nd rowPima
3rd rowPima
4th rowPima
5th rowPima

Common Values

ValueCountFrequency (%)
Pima15848
100.0%

Length

2026-01-13T18:34:40.147307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-13T18:34:40.192803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
pima15848
100.0%

Most occurring characters

ValueCountFrequency (%)
P15848
25.0%
i15848
25.0%
m15848
25.0%
a15848
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)63392
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P15848
25.0%
i15848
25.0%
m15848
25.0%
a15848
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)63392
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P15848
25.0%
i15848
25.0%
m15848
25.0%
a15848
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)63392
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P15848
25.0%
i15848
25.0%
m15848
25.0%
a15848
25.0%

City
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size247.6 KiB
Tucson
15848 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters95088
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTucson
2nd rowTucson
3rd rowTucson
4th rowTucson
5th rowTucson

Common Values

ValueCountFrequency (%)
Tucson15848
100.0%

Length

2026-01-13T18:34:40.246108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-13T18:34:40.292279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
tucson15848
100.0%

Most occurring characters

ValueCountFrequency (%)
T15848
16.7%
u15848
16.7%
c15848
16.7%
s15848
16.7%
o15848
16.7%
n15848
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)95088
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T15848
16.7%
u15848
16.7%
c15848
16.7%
s15848
16.7%
o15848
16.7%
n15848
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)95088
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T15848
16.7%
u15848
16.7%
c15848
16.7%
s15848
16.7%
o15848
16.7%
n15848
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)95088
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T15848
16.7%
u15848
16.7%
c15848
16.7%
s15848
16.7%
o15848
16.7%
n15848
16.7%
Distinct3962
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size247.6 KiB
Minimum2000-01-01 00:00:00
Maximum2010-12-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2026-01-13T18:34:40.352495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:40.455647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

NO2 Units
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size247.6 KiB
Parts per billion
15848 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters269416
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per billion
2nd rowParts per billion
3rd rowParts per billion
4th rowParts per billion
5th rowParts per billion

Common Values

ValueCountFrequency (%)
Parts per billion15848
100.0%

Length

2026-01-13T18:34:40.545915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-13T18:34:40.590111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
parts15848
33.3%
per15848
33.3%
billion15848
33.3%

Most occurring characters

ValueCountFrequency (%)
r31696
11.8%
i31696
11.8%
l31696
11.8%
31696
11.8%
P15848
 
5.9%
s15848
 
5.9%
t15848
 
5.9%
a15848
 
5.9%
p15848
 
5.9%
b15848
 
5.9%
Other values (3)47544
17.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)269416
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r31696
11.8%
i31696
11.8%
l31696
11.8%
31696
11.8%
P15848
 
5.9%
s15848
 
5.9%
t15848
 
5.9%
a15848
 
5.9%
p15848
 
5.9%
b15848
 
5.9%
Other values (3)47544
17.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)269416
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r31696
11.8%
i31696
11.8%
l31696
11.8%
31696
11.8%
P15848
 
5.9%
s15848
 
5.9%
t15848
 
5.9%
a15848
 
5.9%
p15848
 
5.9%
b15848
 
5.9%
Other values (3)47544
17.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)269416
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r31696
11.8%
i31696
11.8%
l31696
11.8%
31696
11.8%
P15848
 
5.9%
s15848
 
5.9%
t15848
 
5.9%
a15848
 
5.9%
p15848
 
5.9%
b15848
 
5.9%
Other values (3)47544
17.6%

NO2 Mean
Numeric time series

High correlation  Non stationary  Seasonal 

Distinct1138
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.813392
Minimum1.75
Maximum37.111111
Zeros0
Zeros (%)0.0%
Memory size247.6 KiB
2026-01-13T18:34:40.685961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.75
5-th percentile6.333333
Q19.916667
median14.083333
Q318.875
95-th percentile25.958333
Maximum37.111111
Range35.361111
Interquartile range (IQR)8.958333

Descriptive statistics

Standard deviation6.0622812
Coefficient of variation (CV)0.4092433
Kurtosis-0.37652269
Mean14.813392
Median Absolute Deviation (MAD)4.36553
Skewness0.49588527
Sum234762.63
Variance36.751254
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.138325482 × 10-9
2026-01-13T18:34:40.973782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2026-01-13T18:34:41.304805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps13
min3 days
max1 week and 1 day
mean5 days, 5 hours and 32 minutes
std1 day, 16 hours and 34 minutes
2026-01-13T18:34:41.411513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
13.95833364
 
0.4%
14.87564
 
0.4%
9.560
 
0.4%
1160
 
0.4%
12.16666756
 
0.4%
10.37556
 
0.4%
12.83333356
 
0.4%
9.87556
 
0.4%
13.33333352
 
0.3%
8.29166752
 
0.3%
Other values (1128)15272
96.4%
ValueCountFrequency (%)
1.754
< 0.1%
2.8333334
< 0.1%
3.3754
< 0.1%
3.3913044
< 0.1%
3.5416674
< 0.1%
3.5454554
< 0.1%
3.5833334
< 0.1%
3.7916674
< 0.1%
3.8754
< 0.1%
3.9166674
< 0.1%
ValueCountFrequency (%)
37.1111114
< 0.1%
364
< 0.1%
35.254
< 0.1%
34.1304354
< 0.1%
34.0416674
< 0.1%
33.254
< 0.1%
33.0416674
< 0.1%
32.758
0.1%
32.5833334
< 0.1%
32.4583334
< 0.1%
2026-01-13T18:34:41.096702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

NO2 1st Max Value
Numeric time series

High correlation  Non stationary  Seasonal 

Distinct139
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.086068
Minimum5
Maximum75
Zeros0
Zeros (%)0.0%
Memory size247.6 KiB
2026-01-13T18:34:41.563224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile14
Q124
median33
Q340
95-th percentile49
Maximum75
Range70
Interquartile range (IQR)16

Descriptive statistics

Standard deviation10.721615
Coefficient of variation (CV)0.33415174
Kurtosis-0.42693519
Mean32.086068
Median Absolute Deviation (MAD)8
Skewness-0.064855878
Sum508500
Variance114.95304
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value7.429366209 × 10-14
2026-01-13T18:34:41.672024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2026-01-13T18:34:41.996075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps13
min3 days
max1 week and 1 day
mean5 days, 5 hours and 32 minutes
std1 day, 16 hours and 34 minutes
2026-01-13T18:34:42.101248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
38580
 
3.7%
39572
 
3.6%
37572
 
3.6%
35560
 
3.5%
36556
 
3.5%
33536
 
3.4%
41520
 
3.3%
34516
 
3.3%
32492
 
3.1%
29480
 
3.0%
Other values (129)10464
66.0%
ValueCountFrequency (%)
58
 
0.1%
620
 
0.1%
720
 
0.1%
840
 
0.3%
988
0.6%
9.84
 
< 0.1%
10100
0.6%
11116
0.7%
12168
1.1%
13172
1.1%
ValueCountFrequency (%)
758
0.1%
694
 
< 0.1%
664
 
< 0.1%
648
0.1%
63.74
 
< 0.1%
638
0.1%
624
 
< 0.1%
614
 
< 0.1%
6012
0.1%
598
0.1%
2026-01-13T18:34:41.798143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

NO2 1st Max Hour
Numeric time series

Non stationary  Seasonal  Zeros 

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.609288
Minimum0
Maximum23
Zeros1628
Zeros (%)10.3%
Memory size247.6 KiB
2026-01-13T18:34:42.399692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median19
Q321
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.2098614
Coefficient of variation (CV)0.56196177
Kurtosis-1.2098117
Mean14.609288
Median Absolute Deviation (MAD)3
Skewness-0.68536687
Sum231528
Variance67.401824
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.746269519 × 10-27
2026-01-13T18:34:42.499018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
2026-01-13T18:34:42.810138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps13
min3 days
max1 week and 1 day
mean5 days, 5 hours and 32 minutes
std1 day, 16 hours and 34 minutes
2026-01-13T18:34:42.944017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
212160
13.6%
221996
12.6%
201776
11.2%
01628
10.3%
61484
9.4%
191416
8.9%
231212
7.6%
181184
7.5%
7760
 
4.8%
5500
 
3.2%
Other values (14)1732
10.9%
ValueCountFrequency (%)
01628
10.3%
1376
 
2.4%
2224
 
1.4%
396
 
0.6%
488
 
0.6%
5500
 
3.2%
61484
9.4%
7760
4.8%
8228
 
1.4%
9100
 
0.6%
ValueCountFrequency (%)
231212
7.6%
221996
12.6%
212160
13.6%
201776
11.2%
191416
8.9%
181184
7.5%
17412
 
2.6%
1656
 
0.4%
1544
 
0.3%
148
 
0.1%
2026-01-13T18:34:42.611592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

NO2 AQI
Numeric time series

High correlation  Non stationary  Seasonal 

Distinct60
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.273852
Minimum5
Maximum73
Zeros0
Zeros (%)0.0%
Memory size247.6 KiB
2026-01-13T18:34:43.110948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile13
Q123
median31
Q338
95-th percentile46
Maximum73
Range68
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.156321
Coefficient of variation (CV)0.33548161
Kurtosis-0.3679722
Mean30.273852
Median Absolute Deviation (MAD)7
Skewness-0.061960753
Sum479780
Variance103.15085
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value7.077983109 × 10-14
2026-01-13T18:34:43.220734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2026-01-13T18:34:43.697400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps13
min3 days
max1 week and 1 day
mean5 days, 5 hours and 32 minutes
std1 day, 16 hours and 34 minutes
2026-01-13T18:34:43.803022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
25768
 
4.8%
42756
 
4.8%
36608
 
3.8%
37588
 
3.7%
35588
 
3.7%
33576
 
3.6%
31564
 
3.6%
34564
 
3.6%
32532
 
3.4%
39520
 
3.3%
Other values (50)9784
61.7%
ValueCountFrequency (%)
58
 
0.1%
620
 
0.1%
720
 
0.1%
8132
0.8%
9100
0.6%
10116
0.7%
11168
1.1%
12176
1.1%
13204
1.3%
14232
1.5%
ValueCountFrequency (%)
738
 
0.1%
674
 
< 0.1%
644
 
< 0.1%
628
 
0.1%
6112
0.1%
604
 
< 0.1%
584
 
< 0.1%
5712
0.1%
568
 
0.1%
5520
0.1%
2026-01-13T18:34:43.343687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

O3 Units
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size247.6 KiB
Parts per million
15848 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters269416
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per million
2nd rowParts per million
3rd rowParts per million
4th rowParts per million
5th rowParts per million

Common Values

ValueCountFrequency (%)
Parts per million15848
100.0%

Length

2026-01-13T18:34:43.911210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-13T18:34:43.955976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
parts15848
33.3%
per15848
33.3%
million15848
33.3%

Most occurring characters

ValueCountFrequency (%)
r31696
11.8%
i31696
11.8%
l31696
11.8%
31696
11.8%
P15848
 
5.9%
s15848
 
5.9%
t15848
 
5.9%
a15848
 
5.9%
p15848
 
5.9%
m15848
 
5.9%
Other values (3)47544
17.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)269416
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r31696
11.8%
i31696
11.8%
l31696
11.8%
31696
11.8%
P15848
 
5.9%
s15848
 
5.9%
t15848
 
5.9%
a15848
 
5.9%
p15848
 
5.9%
m15848
 
5.9%
Other values (3)47544
17.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)269416
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r31696
11.8%
i31696
11.8%
l31696
11.8%
31696
11.8%
P15848
 
5.9%
s15848
 
5.9%
t15848
 
5.9%
a15848
 
5.9%
p15848
 
5.9%
m15848
 
5.9%
Other values (3)47544
17.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)269416
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r31696
11.8%
i31696
11.8%
l31696
11.8%
31696
11.8%
P15848
 
5.9%
s15848
 
5.9%
t15848
 
5.9%
a15848
 
5.9%
p15848
 
5.9%
m15848
 
5.9%
Other values (3)47544
17.6%

O3 Mean
Numeric time series

High correlation  Non stationary  Seasonal 

Distinct1030
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.027467133
Minimum0.004458
Maximum0.058167
Zeros0
Zeros (%)0.0%
Memory size247.6 KiB
2026-01-13T18:34:44.046747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.004458
5-th percentile0.011
Q10.019583
median0.027521
Q30.034917
95-th percentile0.043833
Maximum0.058167
Range0.053709
Interquartile range (IQR)0.015334

Descriptive statistics

Standard deviation0.010185723
Coefficient of variation (CV)0.37083312
Kurtosis-0.63999531
Mean0.027467133
Median Absolute Deviation (MAD)0.007688
Skewness0.057629969
Sum435.29913
Variance0.00010374895
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.279270525 × 10-7
2026-01-13T18:34:44.157405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2026-01-13T18:34:44.480843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps13
min3 days
max1 week and 1 day
mean5 days, 5 hours and 32 minutes
std1 day, 16 hours and 34 minutes
2026-01-13T18:34:44.734967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03037560
 
0.4%
0.02304248
 
0.3%
0.02891744
 
0.3%
0.02741744
 
0.3%
0.0342544
 
0.3%
0.03108344
 
0.3%
0.02620844
 
0.3%
0.03791744
 
0.3%
0.03183344
 
0.3%
0.020540
 
0.3%
Other values (1020)15392
97.1%
ValueCountFrequency (%)
0.0044584
< 0.1%
0.0046254
< 0.1%
0.0047084
< 0.1%
0.004754
< 0.1%
0.0049174
< 0.1%
0.0054
< 0.1%
0.0051254
< 0.1%
0.005254
< 0.1%
0.0052924
< 0.1%
0.0053084
< 0.1%
ValueCountFrequency (%)
0.0581674
< 0.1%
0.0571674
< 0.1%
0.0562924
< 0.1%
0.0560424
< 0.1%
0.0558334
< 0.1%
0.0552924
< 0.1%
0.0549174
< 0.1%
0.0548754
< 0.1%
0.0547088
0.1%
0.05454
< 0.1%
2026-01-13T18:34:44.283322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

O3 1st Max Value
Numeric time series

High correlation  Non stationary  Seasonal 

Distinct75
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.043848309
Minimum0.007
Maximum0.085
Zeros0
Zeros (%)0.0%
Memory size247.6 KiB
2026-01-13T18:34:44.889005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.007
5-th percentile0.024
Q10.035
median0.044
Q30.053
95-th percentile0.064
Maximum0.085
Range0.078
Interquartile range (IQR)0.018

Descriptive statistics

Standard deviation0.012320265
Coefficient of variation (CV)0.2809747
Kurtosis-0.30039261
Mean0.043848309
Median Absolute Deviation (MAD)0.009
Skewness-0.011116711
Sum694.908
Variance0.00015178894
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value9.120626494 × 10-8
2026-01-13T18:34:45.000707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2026-01-13T18:34:45.332095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps13
min3 days
max1 week and 1 day
mean5 days, 5 hours and 32 minutes
std1 day, 16 hours and 34 minutes
2026-01-13T18:34:45.575424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.037520
 
3.3%
0.049492
 
3.1%
0.039488
 
3.1%
0.046484
 
3.1%
0.053472
 
3.0%
0.052468
 
3.0%
0.04468
 
3.0%
0.045468
 
3.0%
0.042464
 
2.9%
0.038460
 
2.9%
Other values (65)11064
69.8%
ValueCountFrequency (%)
0.0074
 
< 0.1%
0.0084
 
< 0.1%
0.00912
 
0.1%
0.0112
 
0.1%
0.0114
 
< 0.1%
0.01216
 
0.1%
0.01332
0.2%
0.01428
0.2%
0.01544
0.3%
0.01660
0.4%
ValueCountFrequency (%)
0.0854
 
< 0.1%
0.0834
 
< 0.1%
0.084
 
< 0.1%
0.0798
 
0.1%
0.07812
 
0.1%
0.0764
 
< 0.1%
0.07544
0.3%
0.07428
0.2%
0.07332
0.2%
0.07220
0.1%
2026-01-13T18:34:45.127259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

O3 1st Max Hour
Numeric time series

Non stationary  Seasonal 

Distinct23
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.7809187
Minimum0
Maximum23
Zeros128
Zeros (%)0.8%
Memory size247.6 KiB
2026-01-13T18:34:45.723006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q19
median10
Q310
95-th percentile11
Maximum23
Range23
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.9101967
Coefficient of variation (CV)0.19529829
Kurtosis21.25303
Mean9.7809187
Median Absolute Deviation (MAD)1
Skewness1.6128452
Sum155008
Variance3.6488515
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.963315146 × 10-29
2026-01-13T18:34:45.811877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
2026-01-13T18:34:46.122989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps13
min3 days
max1 week and 1 day
mean5 days, 5 hours and 32 minutes
std1 day, 16 hours and 34 minutes
2026-01-13T18:34:46.222581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
106416
40.5%
95152
32.5%
112324
 
14.7%
8944
 
6.0%
12276
 
1.7%
7152
 
1.0%
0128
 
0.8%
1372
 
0.5%
2260
 
0.4%
1540
 
0.3%
Other values (13)284
 
1.8%
ValueCountFrequency (%)
0128
 
0.8%
116
 
0.1%
28
 
0.1%
320
 
0.1%
512
 
0.1%
628
 
0.2%
7152
 
1.0%
8944
 
6.0%
95152
32.5%
106416
40.5%
ValueCountFrequency (%)
2336
0.2%
2260
0.4%
2136
0.2%
2036
0.2%
1932
0.2%
184
 
< 0.1%
174
 
< 0.1%
1616
 
0.1%
1540
0.3%
1436
0.2%
2026-01-13T18:34:45.927428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

O3 AQI
Numeric time series

High correlation  Non stationary  Seasonal 

Distinct67
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.393236
Minimum6
Maximum124
Zeros0
Zeros (%)0.0%
Memory size247.6 KiB
2026-01-13T18:34:46.519395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile20
Q130
median37
Q345
95-th percentile64
Maximum124
Range118
Interquartile range (IQR)15

Descriptive statistics

Standard deviation13.635422
Coefficient of variation (CV)0.35515168
Kurtosis4.3510427
Mean38.393236
Median Absolute Deviation (MAD)7
Skewness1.4303327
Sum608456
Variance185.92474
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.51990373 × 10-11
2026-01-13T18:34:46.627467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2026-01-13T18:34:46.957301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps13
min3 days
max1 week and 1 day
mean5 days, 5 hours and 32 minutes
std1 day, 16 hours and 34 minutes
2026-01-13T18:34:47.060152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
31944
 
6.0%
42904
 
5.7%
36872
 
5.5%
47748
 
4.7%
25532
 
3.4%
33488
 
3.1%
39484
 
3.1%
45472
 
3.0%
44468
 
3.0%
34468
 
3.0%
Other values (57)9468
59.7%
ValueCountFrequency (%)
64
 
< 0.1%
74
 
< 0.1%
824
 
0.2%
94
 
< 0.1%
1016
 
0.1%
1132
 
0.2%
1228
 
0.2%
1344
0.3%
1492
0.6%
1540
0.3%
ValueCountFrequency (%)
1244
 
< 0.1%
1194
 
< 0.1%
1114
 
< 0.1%
1098
 
0.1%
10612
 
0.1%
1014
 
< 0.1%
10044
0.3%
9728
0.2%
9332
0.2%
9020
0.1%
2026-01-13T18:34:46.751848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

SO2 Units
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size247.6 KiB
Parts per billion
15848 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters269416
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per billion
2nd rowParts per billion
3rd rowParts per billion
4th rowParts per billion
5th rowParts per billion

Common Values

ValueCountFrequency (%)
Parts per billion15848
100.0%

Length

2026-01-13T18:34:47.166420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-13T18:34:47.213552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
parts15848
33.3%
per15848
33.3%
billion15848
33.3%

Most occurring characters

ValueCountFrequency (%)
r31696
11.8%
i31696
11.8%
l31696
11.8%
31696
11.8%
P15848
 
5.9%
s15848
 
5.9%
t15848
 
5.9%
a15848
 
5.9%
p15848
 
5.9%
b15848
 
5.9%
Other values (3)47544
17.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)269416
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r31696
11.8%
i31696
11.8%
l31696
11.8%
31696
11.8%
P15848
 
5.9%
s15848
 
5.9%
t15848
 
5.9%
a15848
 
5.9%
p15848
 
5.9%
b15848
 
5.9%
Other values (3)47544
17.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)269416
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r31696
11.8%
i31696
11.8%
l31696
11.8%
31696
11.8%
P15848
 
5.9%
s15848
 
5.9%
t15848
 
5.9%
a15848
 
5.9%
p15848
 
5.9%
b15848
 
5.9%
Other values (3)47544
17.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)269416
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r31696
11.8%
i31696
11.8%
l31696
11.8%
31696
11.8%
P15848
 
5.9%
s15848
 
5.9%
t15848
 
5.9%
a15848
 
5.9%
p15848
 
5.9%
b15848
 
5.9%
Other values (3)47544
17.6%

SO2 Mean
Numeric time series

High correlation  Non stationary  Seasonal  Zeros 

Distinct690
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.95393062
Minimum0
Maximum7.625
Zeros678
Zeros (%)4.3%
Memory size247.6 KiB
2026-01-13T18:34:47.313176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0375
Q10.4375
median0.875
Q31.1875
95-th percentile2.385714
Maximum7.625
Range7.625
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.77151951
Coefficient of variation (CV)0.80877948
Kurtosis6.8196654
Mean0.95393062
Median Absolute Deviation (MAD)0.375
Skewness1.9858395
Sum15117.893
Variance0.59524236
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.1593961 × 10-11
2026-01-13T18:34:47.425224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2026-01-13T18:34:47.906416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps13
min3 days
max1 week and 1 day
mean5 days, 5 hours and 32 minutes
std1 day, 16 hours and 34 minutes
2026-01-13T18:34:48.012458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1858
 
5.4%
0678
 
4.3%
0.958333228
 
1.4%
1.125222
 
1.4%
1.083333222
 
1.4%
1.041667222
 
1.4%
0.75204
 
1.3%
0.916667192
 
1.2%
0.875182
 
1.1%
0.125176
 
1.1%
Other values (680)12664
79.9%
ValueCountFrequency (%)
0678
4.3%
0.0041672
 
< 0.1%
0.0083332
 
< 0.1%
0.01254
 
< 0.1%
0.0166672
 
< 0.1%
0.0208334
 
< 0.1%
0.0227272
 
< 0.1%
0.0256
 
< 0.1%
0.0291672
 
< 0.1%
0.0375142
 
0.9%
ValueCountFrequency (%)
7.6252
< 0.1%
7.61252
< 0.1%
6.5833332
< 0.1%
6.5752
< 0.1%
5.6252
< 0.1%
5.5833332
< 0.1%
5.5752
< 0.1%
5.552
< 0.1%
5.54
< 0.1%
5.4752
< 0.1%
2026-01-13T18:34:47.549816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

SO2 1st Max Value
Numeric time series

High correlation  Non stationary  Seasonal  Zeros 

Distinct68
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3709112
Minimum0
Maximum27
Zeros678
Zeros (%)4.3%
Memory size247.6 KiB
2026-01-13T18:34:48.159172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q11
median2
Q33
95-th percentile6.6
Maximum27
Range27
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.2127251
Coefficient of variation (CV)0.9332805
Kurtosis13.042475
Mean2.3709112
Median Absolute Deviation (MAD)1
Skewness2.762295
Sum37574.2
Variance4.8961526
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.972347918 × 10-17
2026-01-13T18:34:48.264625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2026-01-13T18:34:48.749199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps13
min3 days
max1 week and 1 day
mean5 days, 5 hours and 32 minutes
std1 day, 16 hours and 34 minutes
2026-01-13T18:34:48.855124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
14530
28.6%
22420
15.3%
31358
 
8.6%
4906
 
5.7%
1.3900
 
5.7%
1.6794
 
5.0%
0678
 
4.3%
5526
 
3.3%
2.3432
 
2.7%
0.6426
 
2.7%
Other values (58)2878
18.2%
ValueCountFrequency (%)
0678
4.3%
0.126
 
0.2%
0.224
 
0.2%
0.3308
1.9%
0.440
 
0.3%
0.516
 
0.1%
0.6426
2.7%
0.728
 
0.2%
0.824
 
0.2%
0.928
 
0.2%
ValueCountFrequency (%)
272
 
< 0.1%
242
 
< 0.1%
232
 
< 0.1%
222
 
< 0.1%
212
 
< 0.1%
202
 
< 0.1%
19.32
 
< 0.1%
194
< 0.1%
186
< 0.1%
178
0.1%
2026-01-13T18:34:48.391282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

SO2 1st Max Hour
Numeric time series

High correlation  Non stationary  Seasonal  Zeros 

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9435891
Minimum0
Maximum23
Zeros1930
Zeros (%)12.2%
Memory size247.6 KiB
2026-01-13T18:34:49.002490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median10
Q314
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.8581512
Coefficient of variation (CV)0.68970581
Kurtosis-0.85349408
Mean9.9435891
Median Absolute Deviation (MAD)4
Skewness0.2294311
Sum157586
Variance47.034238
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.263201089 × 10-25
2026-01-13T18:34:49.103288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
2026-01-13T18:34:49.410155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps13
min3 days
max1 week and 1 day
mean5 days, 5 hours and 32 minutes
std1 day, 16 hours and 34 minutes
2026-01-13T18:34:49.551378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
112354
14.9%
01930
12.2%
81852
11.7%
21780
11.2%
141170
 
7.4%
201020
 
6.4%
23894
 
5.6%
9568
 
3.6%
10564
 
3.6%
17532
 
3.4%
Other values (14)3184
20.1%
ValueCountFrequency (%)
01930
12.2%
1186
 
1.2%
21780
11.2%
376
 
0.5%
452
 
0.3%
5276
 
1.7%
6328
 
2.1%
7494
 
3.1%
81852
11.7%
9568
 
3.6%
ValueCountFrequency (%)
23894
5.6%
22172
 
1.1%
21222
 
1.4%
201020
6.4%
19214
 
1.4%
18202
 
1.3%
17532
3.4%
16108
 
0.7%
15116
 
0.7%
141170
7.4%
2026-01-13T18:34:49.215140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

SO2 AQI
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct26
Distinct (%)0.3%
Missing7924
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean3.9565876
Minimum0
Maximum39
Zeros456
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size247.6 KiB
2026-01-13T18:34:49.832410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q36
95-th percentile11
Maximum39
Range39
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.859273
Coefficient of variation (CV)0.97540441
Kurtosis8.7921131
Mean3.9565876
Median Absolute Deviation (MAD)2
Skewness2.2771623
Sum31352
Variance14.893988
MonotonicityNot monotonic
2026-01-13T18:34:49.912489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
12486
 
15.7%
31734
 
10.9%
41060
 
6.7%
6740
 
4.7%
7462
 
2.9%
0456
 
2.9%
9322
 
2.0%
10190
 
1.2%
11172
 
1.1%
1392
 
0.6%
Other values (16)210
 
1.3%
(Missing)7924
50.0%
ValueCountFrequency (%)
0456
 
2.9%
12486
15.7%
31734
10.9%
41060
6.7%
6740
 
4.7%
7462
 
2.9%
9322
 
2.0%
10190
 
1.2%
11172
 
1.1%
1392
 
0.6%
ValueCountFrequency (%)
392
 
< 0.1%
342
 
< 0.1%
332
 
< 0.1%
312
 
< 0.1%
302
 
< 0.1%
292
 
< 0.1%
274
< 0.1%
266
< 0.1%
248
0.1%
238
0.1%

CO Units
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size247.6 KiB
Parts per million
15848 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters269416
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per million
2nd rowParts per million
3rd rowParts per million
4th rowParts per million
5th rowParts per million

Common Values

ValueCountFrequency (%)
Parts per million15848
100.0%

Length

2026-01-13T18:34:49.994388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-13T18:34:50.040049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
parts15848
33.3%
per15848
33.3%
million15848
33.3%

Most occurring characters

ValueCountFrequency (%)
r31696
11.8%
i31696
11.8%
l31696
11.8%
31696
11.8%
P15848
 
5.9%
s15848
 
5.9%
t15848
 
5.9%
a15848
 
5.9%
p15848
 
5.9%
m15848
 
5.9%
Other values (3)47544
17.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)269416
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r31696
11.8%
i31696
11.8%
l31696
11.8%
31696
11.8%
P15848
 
5.9%
s15848
 
5.9%
t15848
 
5.9%
a15848
 
5.9%
p15848
 
5.9%
m15848
 
5.9%
Other values (3)47544
17.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)269416
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r31696
11.8%
i31696
11.8%
l31696
11.8%
31696
11.8%
P15848
 
5.9%
s15848
 
5.9%
t15848
 
5.9%
a15848
 
5.9%
p15848
 
5.9%
m15848
 
5.9%
Other values (3)47544
17.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)269416
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r31696
11.8%
i31696
11.8%
l31696
11.8%
31696
11.8%
P15848
 
5.9%
s15848
 
5.9%
t15848
 
5.9%
a15848
 
5.9%
p15848
 
5.9%
m15848
 
5.9%
Other values (3)47544
17.6%

CO Mean
Numeric time series

High correlation  Non stationary  Seasonal 

Distinct446
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.35347102
Minimum0
Maximum1.3625
Zeros4
Zeros (%)< 0.1%
Memory size247.6 KiB
2026-01-13T18:34:50.140279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.166667
Q10.241667
median0.316667
Q30.429167
95-th percentile0.6625
Maximum1.3625
Range1.3625
Interquartile range (IQR)0.1875

Descriptive statistics

Standard deviation0.16011454
Coefficient of variation (CV)0.45297784
Kurtosis2.8355115
Mean0.35347102
Median Absolute Deviation (MAD)0.0875
Skewness1.39186
Sum5601.8088
Variance0.025636666
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.540132146 × 10-9
2026-01-13T18:34:50.255169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2026-01-13T18:34:50.586434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps13
min3 days
max1 week and 1 day
mean5 days, 5 hours and 32 minutes
std1 day, 16 hours and 34 minutes
2026-01-13T18:34:50.697260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.2444
 
2.8%
0.266667290
 
1.8%
0.3268
 
1.7%
0.241667262
 
1.7%
0.2625240
 
1.5%
0.233333226
 
1.4%
0.254167218
 
1.4%
0.220833216
 
1.4%
0.258333212
 
1.3%
0.229167208
 
1.3%
Other values (436)13264
83.7%
ValueCountFrequency (%)
04
< 0.1%
0.0041672
< 0.1%
0.0083334
< 0.1%
0.0166672
< 0.1%
0.0208332
< 0.1%
0.0252
< 0.1%
0.0291672
< 0.1%
0.03754
< 0.1%
0.0416674
< 0.1%
0.0458334
< 0.1%
ValueCountFrequency (%)
1.36252
< 0.1%
1.3416672
< 0.1%
1.31252
< 0.1%
1.1791672
< 0.1%
1.1752
< 0.1%
1.1708332
< 0.1%
1.1666672
< 0.1%
1.1565222
< 0.1%
1.1208332
< 0.1%
1.11252
< 0.1%
2026-01-13T18:34:50.380525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

CO 1st Max Value
Real number (ℝ)

High correlation 

Distinct47
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.70338213
Minimum0
Maximum5.4
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size247.6 KiB
2026-01-13T18:34:50.799702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q10.4
median0.6
Q30.825
95-th percentile1.7
Maximum5.4
Range5.4
Interquartile range (IQR)0.425

Descriptive statistics

Standard deviation0.50552689
Coefficient of variation (CV)0.71870875
Kurtosis9.8892721
Mean0.70338213
Median Absolute Deviation (MAD)0.2
Skewness2.5119996
Sum11147.2
Variance0.25555744
MonotonicityNot monotonic
2026-01-13T18:34:50.892739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0.32318
14.6%
0.42318
14.6%
0.52138
13.5%
0.61724
10.9%
0.71326
8.4%
0.81064
6.7%
0.2960
6.1%
0.9786
 
5.0%
1550
 
3.5%
1.1512
 
3.2%
Other values (37)2152
13.6%
ValueCountFrequency (%)
04
 
< 0.1%
0.134
 
0.2%
0.2960
6.1%
0.32318
14.6%
0.42318
14.6%
0.52138
13.5%
0.61724
10.9%
0.71326
8.4%
0.81064
6.7%
0.9786
 
5.0%
ValueCountFrequency (%)
5.44
< 0.1%
52
 
< 0.1%
4.82
 
< 0.1%
4.42
 
< 0.1%
4.32
 
< 0.1%
4.22
 
< 0.1%
4.12
 
< 0.1%
3.92
 
< 0.1%
3.86
< 0.1%
3.74
< 0.1%

CO 1st Max Hour
Real number (ℝ)

Zeros 

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.2689298
Minimum0
Maximum23
Zeros4740
Zeros (%)29.9%
Negative0
Negative (%)0.0%
Memory size247.6 KiB
2026-01-13T18:34:50.971125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7
Q320
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)20

Descriptive statistics

Standard deviation8.857153
Coefficient of variation (CV)0.9555745
Kurtosis-1.4572977
Mean9.2689298
Median Absolute Deviation (MAD)7
Skewness0.43173809
Sum146894
Variance78.449159
MonotonicityNot monotonic
2026-01-13T18:34:51.204399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
04740
29.9%
61614
 
10.2%
71544
 
9.7%
221290
 
8.1%
231124
 
7.1%
211026
 
6.5%
20844
 
5.3%
5618
 
3.9%
19576
 
3.6%
8564
 
3.6%
Other values (14)1908
12.0%
ValueCountFrequency (%)
04740
29.9%
1522
 
3.3%
2220
 
1.4%
384
 
0.5%
468
 
0.4%
5618
 
3.9%
61614
 
10.2%
71544
 
9.7%
8564
 
3.6%
9188
 
1.2%
ValueCountFrequency (%)
231124
7.1%
221290
8.1%
211026
6.5%
20844
5.3%
19576
3.6%
18296
 
1.9%
1792
 
0.6%
1636
 
0.2%
1528
 
0.2%
146
 
< 0.1%

CO AQI
Numeric time series

High correlation  Missing  Non stationary  Seasonal 

Distinct26
Distinct (%)0.3%
Missing7924
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean6.272842
Minimum0
Maximum31
Zeros2
Zeros (%)< 0.1%
Memory size247.6 KiB
2026-01-13T18:34:51.314618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median6
Q38
95-th percentile13
Maximum31
Range31
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.5417867
Coefficient of variation (CV)0.56462233
Kurtosis4.0889428
Mean6.272842
Median Absolute Deviation (MAD)2
Skewness1.5383985
Sum49706
Variance12.544253
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.845126501 × 10-5
2026-01-13T18:34:51.410964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
2026-01-13T18:34:51.705682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps13
min3 days
max1 week and 1 day
mean5 days, 5 hours and 32 minutes
std1 day, 16 hours and 34 minutes
2026-01-13T18:34:51.799216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
31536
 
9.7%
51396
 
8.8%
61188
 
7.5%
7842
 
5.3%
2710
 
4.5%
8674
 
4.3%
9472
 
3.0%
10344
 
2.2%
13184
 
1.2%
11180
 
1.1%
Other values (16)398
 
2.5%
(Missing)7924
50.0%
ValueCountFrequency (%)
02
 
< 0.1%
124
 
0.2%
2710
4.5%
31536
9.7%
51396
8.8%
61188
7.5%
7842
5.3%
8674
4.3%
9472
 
3.0%
10344
 
2.2%
ValueCountFrequency (%)
314
 
< 0.1%
272
 
< 0.1%
262
 
< 0.1%
252
 
< 0.1%
244
 
< 0.1%
238
 
0.1%
2216
0.1%
2018
0.1%
1912
 
0.1%
1838
0.2%
2026-01-13T18:34:51.510696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

Interactions

2026-01-13T18:34:36.185935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:19.155505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:20.368460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:21.468287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:22.650593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:23.731765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:24.791956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:26.083394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:27.108506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:28.303441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:29.440954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:30.495901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:31.695915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:32.823111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:34.082137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:35.158306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:36.254000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:19.220453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:20.439109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:21.534119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:22.717724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:23.800573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:24.865777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:26.147991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:27.175526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:28.376883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:29.506865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:30.560721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:31.768813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:32.896258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:34.150854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:35.222705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:36.322303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:19.291914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:20.509285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:21.598340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:22.785288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:23.869865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:24.937325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:26.213343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:27.243608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:28.450992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:29.574280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:30.628406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:31.839966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:32.966546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:34.217901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:35.289347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:36.383778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:19.358365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:20.573583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:21.658856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:22.849281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:23.934738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:25.155453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:26.273866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:27.307526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:28.518726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:29.635566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:30.688013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:31.908989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:33.035201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:34.283986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:35.348829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:36.451523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:19.427808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:20.646453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:21.722686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:22.917440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:24.003428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:25.227660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:26.338409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:27.374338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:28.591118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:29.702910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:30.931804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:31.979169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:33.106339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:34.352098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:35.415796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:36.517358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:19.605366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:20.713805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:21.787520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:22.983809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:24.066393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:25.298949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:26.403572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:27.438612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:28.660654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:29.767247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:30.995120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:32.050487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:33.177583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:34.421609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:35.478326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:36.738389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:19.680090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:20.787929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:21.856864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:23.056787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:24.136868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:25.372675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:26.471536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:27.508248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:28.735759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:29.837389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:31.063989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:32.124629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:33.250720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:34.493220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:35.546768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:36.802090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:19.745785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:20.852518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:21.919383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:23.121806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:24.198507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:25.441702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:26.532096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:27.568983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:28.803555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:29.900875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:31.123796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:32.193595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:33.318222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:34.560473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:35.607924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:36.869001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:19.814218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:20.921231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:21.981015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:23.187778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:24.262879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:25.511431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:26.593824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:27.630486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:28.876023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:29.965260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:31.186629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:32.262289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:33.385421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:34.625414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:35.669406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:36.940170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:19.886420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:20.992084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:22.050753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:23.257369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:24.332556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:25.587085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:26.663389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:27.700009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:28.949128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:30.037929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:31.253834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:32.337597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:33.460817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:34.696251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:35.739006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:37.008105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:19.955026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:21.061141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:22.113654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:23.325712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:24.396613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:25.657046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:26.725908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:27.763628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:29.018565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:30.101018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:31.317825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:32.406676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:33.530019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:34.760315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:35.800890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:37.071644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:20.020747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:21.127049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:22.173905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:23.390779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:24.460620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:25.725144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:26.786561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:27.972936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:29.085327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:30.165285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:31.377212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:32.474918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:33.735016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:34.825121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:35.862660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:37.143523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:20.093695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:21.199540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:22.397979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:23.462545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:24.530014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:25.801187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:26.854370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:28.042462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:29.160237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:30.234182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:31.445045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:32.546294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:33.808028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:34.895199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:35.930641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:37.211107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:20.165680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:21.269948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:22.464117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:23.531881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:24.599403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:25.873967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:26.920610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:28.111173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:29.233393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:30.303670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:31.509924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:32.620032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:33.879346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:34.964712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:35.999526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:37.278675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:20.234923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:21.337307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:22.527081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:23.599821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:24.662357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:25.944159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:26.984409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:28.176229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:29.303598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:30.367398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:31.571229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:32.688195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:33.946880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:35.027089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:36.061656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:37.341338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:20.299624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:21.401620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:22.586256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:23.662868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:24.726851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:26.011138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:27.043360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:28.238353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:29.369756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:30.431816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:31.633371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:32.755158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:34.013948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:35.089431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T18:34:36.121092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-01-13T18:34:51.900617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
CO 1st Max HourCO 1st Max ValueCO AQICO MeanNO2 1st Max HourNO2 1st Max ValueNO2 AQINO2 MeanO3 1st Max HourO3 1st Max ValueO3 AQIO3 MeanSO2 1st Max HourSO2 1st Max ValueSO2 AQISO2 Mean
CO 1st Max Hour1.0000.3800.2720.1930.3240.3570.3570.277-0.059-0.110-0.110-0.2270.1470.0740.0640.084
CO 1st Max Value0.3801.0001.0000.8380.1020.6970.6970.752-0.116-0.396-0.396-0.5800.2480.3700.3480.403
CO AQI0.2721.0001.0000.9130.0500.6900.6910.783-0.142-0.447-0.447-0.6180.2620.3960.3730.433
CO Mean0.1930.8380.9131.0000.0660.6500.6510.795-0.103-0.417-0.416-0.5880.2320.3920.3790.413
NO2 1st Max Hour0.3240.1020.0500.0661.0000.2980.2980.1920.0960.0520.052-0.1250.078-0.020-0.023-0.008
NO2 1st Max Value0.3570.6970.6900.6500.2981.0001.0000.827-0.015-0.121-0.121-0.4100.2690.3210.3020.366
NO2 AQI0.3570.6970.6910.6510.2981.0001.0000.826-0.015-0.120-0.120-0.4090.2680.3220.3030.367
NO2 Mean0.2770.7520.7830.7950.1920.8270.8261.000-0.055-0.396-0.396-0.6660.2780.3720.3530.418
O3 1st Max Hour-0.059-0.116-0.142-0.1030.096-0.015-0.015-0.0551.0000.3130.3130.223-0.067-0.035-0.025-0.077
O3 1st Max Value-0.110-0.396-0.447-0.4170.052-0.121-0.120-0.3960.3131.0001.0000.888-0.127-0.162-0.139-0.184
O3 AQI-0.110-0.396-0.447-0.4160.052-0.121-0.120-0.3960.3131.0001.0000.888-0.127-0.161-0.139-0.184
O3 Mean-0.227-0.580-0.618-0.588-0.125-0.410-0.409-0.6660.2230.8880.8881.000-0.229-0.255-0.230-0.292
SO2 1st Max Hour0.1470.2480.2620.2320.0780.2690.2680.278-0.067-0.127-0.127-0.2291.0000.4010.5200.204
SO2 1st Max Value0.0740.3700.3960.392-0.0200.3210.3220.372-0.035-0.162-0.161-0.2550.4011.0000.9990.767
SO2 AQI0.0640.3480.3730.379-0.0230.3020.3030.353-0.025-0.139-0.139-0.2300.5200.9991.0000.732
SO2 Mean0.0840.4030.4330.413-0.0080.3660.3670.418-0.077-0.184-0.184-0.2920.2040.7670.7321.000

Missing values

2026-01-13T18:34:37.480022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-13T18:34:37.650725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2026-01-13T18:34:37.799957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

State CodeCounty CodeSite NumAddressStateCountyCityDate LocalNO2 UnitsNO2 MeanNO2 1st Max ValueNO2 1st Max HourNO2 AQIO3 UnitsO3 MeanO3 1st Max ValueO3 1st Max HourO3 AQISO2 UnitsSO2 MeanSO2 1st Max ValueSO2 1st Max HourSO2 AQICO UnitsCO MeanCO 1st Max ValueCO 1st Max HourCO AQI
2000-01-0141910111237 S. BEVERLY , TUCSONArizonaPimaTucson2000-01-01Parts per billion15.20833338.01936Parts per million0.0229170.039933Parts per billion1.8333334.086.0Parts per million0.3833331.319NaN
2000-01-0141910111237 S. BEVERLY , TUCSONArizonaPimaTucson2000-01-01Parts per billion15.20833338.01936Parts per million0.0229170.039933Parts per billion1.8333334.086.0Parts per million0.3210530.7238.0
2000-01-0141910111237 S. BEVERLY , TUCSONArizonaPimaTucson2000-01-01Parts per billion15.20833338.01936Parts per million0.0229170.039933Parts per billion1.8125002.32NaNParts per million0.3833331.319NaN
2000-01-0141910111237 S. BEVERLY , TUCSONArizonaPimaTucson2000-01-01Parts per billion15.20833338.01936Parts per million0.0229170.039933Parts per billion1.8125002.32NaNParts per million0.3210530.7238.0
2000-01-0241910111237 S. BEVERLY , TUCSONArizonaPimaTucson2000-01-02Parts per billion18.50000038.01936Parts per million0.0157080.0311026Parts per billion1.3750002.073.0Parts per million0.3312500.78NaN
2000-01-0241910111237 S. BEVERLY , TUCSONArizonaPimaTucson2000-01-02Parts per billion18.50000038.01936Parts per million0.0157080.0311026Parts per billion1.3750002.073.0Parts per million0.4388890.819.0
2000-01-0241910111237 S. BEVERLY , TUCSONArizonaPimaTucson2000-01-02Parts per billion18.50000038.01936Parts per million0.0157080.0311026Parts per billion1.3625002.011NaNParts per million0.3312500.78NaN
2000-01-0241910111237 S. BEVERLY , TUCSONArizonaPimaTucson2000-01-02Parts per billion18.50000038.01936Parts per million0.0157080.0311026Parts per billion1.3625002.011NaNParts per million0.4388890.819.0
2000-01-0441910111237 S. BEVERLY , TUCSONArizonaPimaTucson2000-01-04Parts per billion31.52173942.01040Parts per million0.0134170.023819Parts per billion2.3333334.0186.0Parts per million0.9250001.919NaN
2000-01-0441910111237 S. BEVERLY , TUCSONArizonaPimaTucson2000-01-04Parts per billion31.52173942.01040Parts per million0.0134170.023819Parts per billion2.3333334.0186.0Parts per million1.0142861.12013.0
State CodeCounty CodeSite NumAddressStateCountyCityDate LocalNO2 UnitsNO2 MeanNO2 1st Max ValueNO2 1st Max HourNO2 AQIO3 UnitsO3 MeanO3 1st Max ValueO3 1st Max HourO3 AQISO2 UnitsSO2 MeanSO2 1st Max ValueSO2 1st Max HourSO2 AQICO UnitsCO MeanCO 1st Max ValueCO 1st Max HourCO AQI
2010-12-2941910111237 S. BEVERLY , TUCSONArizonaPimaTucson2010-12-29Parts per billion12.93478330.5028Parts per million0.0299580.0382032Parts per billion0.3304350.690.0Parts per million0.2260870.68NaN
2010-12-2941910111237 S. BEVERLY , TUCSONArizonaPimaTucson2010-12-29Parts per billion12.93478330.5028Parts per million0.0299580.0382032Parts per billion0.3304350.690.0Parts per million0.3250000.708.0
2010-12-3041910111237 S. BEVERLY , TUCSONArizonaPimaTucson2010-12-30Parts per billion10.85416738.22136Parts per million0.0265000.037931Parts per billion0.2125000.35NaNParts per million0.2291670.521NaN
2010-12-3041910111237 S. BEVERLY , TUCSONArizonaPimaTucson2010-12-30Parts per billion10.85416738.22136Parts per million0.0265000.037931Parts per billion0.2125000.35NaNParts per million0.1916670.4235.0
2010-12-3041910111237 S. BEVERLY , TUCSONArizonaPimaTucson2010-12-30Parts per billion10.85416738.22136Parts per million0.0265000.037931Parts per billion0.2500000.330.0Parts per million0.2291670.521NaN
2010-12-3041910111237 S. BEVERLY , TUCSONArizonaPimaTucson2010-12-30Parts per billion10.85416738.22136Parts per million0.0265000.037931Parts per billion0.2500000.330.0Parts per million0.1916670.4235.0
2010-12-3141910111237 S. BEVERLY , TUCSONArizonaPimaTucson2010-12-31Parts per billion19.09166731.2729Parts per million0.0186840.0301125Parts per billion0.2666670.4140.0Parts per million0.3500000.405.0
2010-12-3141910111237 S. BEVERLY , TUCSONArizonaPimaTucson2010-12-31Parts per billion19.09166731.2729Parts per million0.0186840.0301125Parts per billion0.2500000.38NaNParts per million0.3333330.87NaN
2010-12-3141910111237 S. BEVERLY , TUCSONArizonaPimaTucson2010-12-31Parts per billion19.09166731.2729Parts per million0.0186840.0301125Parts per billion0.2666670.4140.0Parts per million0.3333330.87NaN
2010-12-3141910111237 S. BEVERLY , TUCSONArizonaPimaTucson2010-12-31Parts per billion19.09166731.2729Parts per million0.0186840.0301125Parts per billion0.2500000.38NaNParts per million0.3500000.405.0